Index terms: Geographical information systems (GIS), water resources system, remote sensing, storage capacity area relationship, small dams

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1 SMALL RESERVOIR STORAGE CAPACITIES USING REMOTELY SENSED SURFACE AREAS: A CASE OF THE PRETO RIVER BASIN, BRAZIL Lineu Neiva Rodrigues, Edson Eyji Sano, Denílson Pereira Passo, Juliana Marioti (Embrapa Cerrados, BR 020, Km 18, Caixa Postal 08223, Planaltina, DF. lineu@cpac.embrapa.br) Index terms: Geographical information systems (GIS), water resources system, remote sensing, storage capacity area relationship, small dams Introduction Reservoirs or dams are proper infrastructure to deal with hydrological situations like the one observed in the Preto River basin (PRB), where the available natural flow in the stream is sometimes less than the flow required for water supply or irrigation, and water can be stored from a time when there is surplus, for example, from a wet season to a dry season. They can be classified based on their size, type of use or materials used in the construction or on the hydraulic design. This paper deals with small (those with a height below 15m) storage or diversion nonoverflow earthfill reservoirs. In recent decades, thousands of these small reservoirs (SR) were built in the São Francisco Basin (SF). In the Preto River Basin, which represents only 1.6% of the area of the SF, Rodrigues et al. (2007), using remote sensing technique, identified 253 small reservoirs. The government of the Federal District of Brazil is planning to build 30 new small dams in the PRB to increase the irrigated agriculture in the region. It is necessary, however, to obtain knowledge about the physical characteristics of the existing reservoirs before building new ones. The lack of information about them is a critical constraint in decision-making processes regarding planning and management of water resources. Estimating the storage capacity of dams is essential to the responsible management of water diversion. Most of these SR in the PRB has never been evaluated; making difficult the estimates of their current storage capacity as sediment deposition gradually reduces it over time. Ideally SR should be surveyed from time to time in such way the impact of sedimentation on their storage capacity could be assessed (Rodrigues et al., 2008).

2 The current techniques for estimating reservoir capacity estimates include both direct (reservoir surveys) and indirect methods (use of topographical maps). Liebe et al. (2005) carried out a study to understand the impact of small dams in the water availability in the Upper East Region of Ghana. They developed a simple method, based on remote sensing, for estimating and monitoring the storage volumes of small reservoirs based on their surface areas. Rodrigues et al. (2007) also applied remote sensing technique to estimate the number and surface area of small dams in the PRB, finding a good correlation (R2 = 92%) between remote sensing and field measurements of reservoir surface areas. Thus, there are indications that there is room for improvement as far as research on small reservoirs is concerned in Brazil and a need to establish a model equation to estimate reservoir storage capacities of small reservoirs using their remotely sensed surface areas. The objectives of this study are: i) to develop a methodology to estimate small reservoir capacities as a function of their remotely sensed surface areas in the Preto River Basin, a sub-basin of the São Francisco Basin, Brazil; and ii) to estimate the total water storage capacity of small reservoirs in the Basin. Material and Methods The Preto River Basin drainage area is roughly 10,500 square kilometers. It is located in the central portion of Brazil, on the western side of the middle portion of the São Francisco Basin. The spatial distribution and the surface areas of the reservoirs in the Basin were determined by Rodrigues et al. (2007). The authors used remote sensing technique to study the spatial distribution and to calculate the surface area of SR in the PRB. In their study, the shape and size of the reservoir surface area were determined by walking around each reservoir with a handheld GPS receiver, taking large number of points along the shoreline. The initial database consisted of 253 small reservoirs, but only the ones with surface area varying from 1 to 47 hectares were considered, totalizing 147 small reservoirs. For the benefit of a good distribution of samples over the full range of the different reservoir sizes, the dataset was split into three categories. Category 1, with 68 reservoirs, consisted of reservoirs with surface area ranging from 1 to 3 ha; 51 reservoirs, with surface area varying from 3 to 10 ha, were set in Category 2. Finally, 28 reservoirs were set in Category 3, which has reservoirs with surface area varying from 10 to 47 ha. To have an extensive sample set to ensure sufficient coverage, 42 out of

3 the 147, i.e., 28.5% of the total population of SR, were evaluated. Thus, 29.4% of the reservoirs were measured in both categories 1 and 2, and 25.0% in category 3. The selected reservoirs were measured during three fieldworks conducted from March to April and October to November of 2006 and from March to May of The shape and size of the reservoir surface areas were determined by walking around each reservoir with a handheld GPS receiver, taking large number of points along the shoreline. Following this step, water depths were measured in several places inside the reservoir using a plummet. The coordinates of each measurement were taking by the GPS receiver. Depth measurements of reservoirs were conducted to estimate their volume. The measurement entails finding a reservoir s deepest point from which it can be interpolated to the shores. Care was taken to achieve good coverage of points well spread over the reservoir while focusing on the parts close to the dam wall, where the deepest point is generally found. The biggest problem turned out to be navigation with the most underestimated factor being the wind causing a lot of drift. Dam storage volumes of the surveyed reservoirs were calculated using a 3D-model in the Surfer interpolation software package. Therefore, the outline and depth data were merged and organized in a xyz ASCII table (negative z score). Using Kriging technique, the point data was interpolated over a 1-meter regular grid. To calculate the volume-area relationship, both storage capacity and surface area were transformed into log equations and then the power relationship with the respective R 2 calculated. The goodness of the fit between measured and modeled volumes was also evaluated using the model efficiency measure of Nash Suttcliffe (1970), Eq. 1. N i= 1 = 1 N ( Vo Ve ) ( Voi Vo) i= 1 i i 2 2 NSE (1) Where Vo i is the real volume, Veithe estimated volume and Vo the mean real volume. NSE value = 1 indicates perfect model performance and NSE value = 0 indicates that model is, on average, performing only as good as the use of the mean target value as prediction, and an NSE value <0 indicates an altogether questionable choice of model (Schaefli and Gupta, 2007).

4 The total storage capacities of the small reservoirs in the Preto River Basin were calculated using the volume-area relationship and the remote sensing reservoir surface areas. Results and Discussion Figure 1 presents a general storage capacity-remotely sensed area relationship, case (a), that was obtained using all data and relationships specifically for the States of Minas Gerais and Goiás and for the Federal District. In this figure, is also presented the total number of reservoir considered in the analysis as well as the number of reservoirs belonging in each category. A power relationship (Volume = a Area n ) was obtained when it was considered all data, case (a) in figure 1, and when was considered only data from the Federal District, case (c). A linear relationship (Volume = a Area - n) was obtained in the other two cases, using data only from the State of Minas Gerais, case (b), and when was used data only from the State of Goiás. It was also tried to develop storage capacity-area relationships for each category, but the precision was not good. The best R 2 obtained for this situation was 0.72 for category 3. Even though the Basin is the unit where water resource should be managed, for political reasons is interesting to establish area-volume relationships for each State of the Basin. The R 2 varied from 0.76 for the Federal District to 0.98 for Minas Gerais State, indicating a good precision of the relationship obtained. The coefficients for the relationships are presented in Figure 1. The NSE calculated considering the whole dataset indicates that the model explains 81% of the measured variance. The percentile error, calculated considering the total volume stored based on the measurements and the total volume calculated using the general relationship, was equal to 10.6%. Based on the equation presented in Figure 1 box (a) and in the surface area of small reservoirs obtained from satellite imageries, it was verified that, at full capacity, the Preto River Basin s 147 small reservoirs can capture up to 189 x 10 5 m 3 of water.

5 (a) All states (b) Minas Gerais Volume = 12,802 Area R 2 = N = Number of reservoirs evaluated = 42 Category 1 (1 surface area < 3) = 19; Category 2 (3 surface area < 10) = 15; Category 3 (10 surface area < 47) = Volume = 20,083 Area R 2 = N = Number of reservoirs evaluated = 9 Category 1 (1 surface area < 3) = 2; Category 2 (3 surface area < 10) = 4; Category 3 (10 surface area < 47) = 2. (c) Federal District (d) Goiás Volume = 12,255 Area R 2 = Volume = 26,552 Area R 2 = N = Number of reservoirs evaluated = 19 Category 1 (1 surface area < 3) = 14; Category 2 (3 surface area < 10) = 4; Category 3 (10 surface area < 47) = N = Number of reservoirs evaluated = 14 Category 1 (1 surface area < 3) = 3; Category 2 (3 surface area < 10) = 7; Category 3 (10 surface area < 47) = 5. Figure 1. Storage capacity and remotely sensed area relationships for all surveyed small reservoirs in the Preto River Basin (a), for the States of Minas Gerais (b), State of Goiás (d), and for the Federal District (c).

6 Conclusions Remote sensing was found to be a suitable means to detect small reservoirs and accurately measure their surface areas. The general relationship between measured reservoir volumes and their remotely sensed surface areas showed good accuracy. The fact that it explained 83% of measured variance gives, in some extent, confidence in its use, especially for reservoirs that don t have detailed information. Combining this relationship with periodical satellite-based reservoir area measurements will allow hydrologists and planners to have clear picture of water resource system in the Preto River Basin, especially in ungauged sub-basins. At full capacity, the water that the Preto River Basin s 147 small reservoirs can capture is small. These infrastructures, however, act as a set of well-distributed and easily accessible water source systems that have multi-purpose uses, contributing to reduce people s vulnerability and improve their livelihood. References LIEBE, J., VAN DE GIESEN, N., ANDREINI, M. Estimation of Small Reservoir Storage Capacities in a semi-arid environment. A case study in the Upper East Region of Ghana. Physics and Chemistry of the Earth, vol.30, p , NASH, J.E.; SUTCLIFFE, J.V. River flow forecasting though conceptual models. Part 1 A discussion of principles. Journal of Hydrology, v.10, n.3, , RODRIGUES, L.N.; SANO, E.E.; SOCCOL, O.J.; VAN DE GIESEN, N.; ANDREINI, M. Water storage capacity of small reservoirs in the Rio Preto Basin, Brazil. In: CIGR - International Conference of Agricultural Engineering and XXXVII Congresso Brasileiro de Engenharia Agrícola, 2008, Foz do Iguaçu. RODRIGUES, L.N.; SANO, E.E.; AZEVEDO, J.A.; SILVA, E.M. Distribuição espacial e área máxima do espelho d água de pequenas barragens de terra na Bacia do Rio Preto. Espaço e Geografia, v. 10, p , Acknowledgments: The presented research was carried out as part of the Small Reservoir Project. We gratefully acknowledge financial support of Advisory Service on Agricultural Research for Development (BEAF) through the Challenge Program on Water and Food.